Correlation detection strategies in microbial data sets vary widely in sensitivity and precision

Biogeography, Assembly Patterns, Driving Factors, and Interactions of Archaeal Community in Mangrove Sediments

Archaea are a major part of Earth's life. They are believed to play important roles in nutrient biogeochemical cycling in the mangrove. However, only a few studies on the archaeal community in mangroves have been reported. In particular, the assembly processes and interaction patterns that impact the archaeal communities in mangroves have not been investigated to date. Here, the biogeography, assembly patterns, and driving factors of archaeal communities in seven representative mangroves across southeastern China were systematically analyzed. The analysis revealed that the archaeal community is more diverse in surface sediments than in subsurface sediments, and more diverse in mangroves at low latitudes than at high latitudes, with Woesearchaeota and Bathyarchaeota as the most diverse and most abundant phyla, respectively. Beta nearest-taxon index analysis suggested a determinant role of homogeneous selection on the overall archaeon community in all mangroves and in each individual mangrove. In addition, the conditionally rare taxon community was strongly shaped by homogeneous selection, while stochastic processes shaped the dominant taxon and always-rare taxon communities. Further, a moderate effect of environmental selection on the archaeal community was noted, with the smallest effect on the always-rare taxon community. Mangrove location, mean annual temperature, and salinity were the major factors that greatly affected the community composition. Finally, network analysis revealed comprehensive cooccurrence relationships in the archaeal community, with a crucial role of Bathyarchaeota. This study expands the understanding of the biogeography, assembly patterns, driving factors, and cooccurrence relationships of the mangrove archaeal community and inspires functional exploration of archaeal resources in mangrove sediments. IMPORTANCE As a key microbial community component with important ecological roles, archaea merit the attention of biologists and ecologists. The mechanisms controlling microbial community diversity, composition, and biogeography are central to microbial ecology but poorly understood. Mangroves are located at the land-ocean interface and are an ideal environment for examining the above questions. We here provided the first-ever overview of archaeal community structure and biogeography in mangroves located along an over-9,000-km coastline of southeastern China. We observed that archaeal diversity in low-latitude mangroves was higher than that in high-latitude mangroves. Furthermore, our data indicated that homogeneous selection strongly controlled the assembly of the overall and conditionally rare taxon communities in mangrove sediments, while the dominant taxon and always-rare taxon communities were mainly controlled by dispersal limitation.

A zero inflated log-normal model for inference of sparse microbial association networks

The advent of high-throughput metagenomic sequencing has prompted the development of efficient taxonomic profiling methods allowing to measure the presence, abundance and phylogeny of organisms in a wide range of environmental samples. Multivariate sequence-derived abundance data further has the potential to enable inference of ecological associations between microbial populations, but several technical issues need to be accounted for, like the compositional nature of the data, its extreme sparsity and overdispersion, as well as the frequent need to operate in under-determined regimes. The ecological network reconstruction problem is frequently cast into the paradigm of Gaussian Graphical Models (GGMs) for which efficient structure inference algorithms are available, like the graphical lasso and neighborhood selection. Unfortunately, GGMs or variants thereof can not properly account for the extremely sparse patterns occurring in real-world metagenomic taxonomic profiles. In particular, structural zeros (as opposed to sampling zeros) corresponding to true absences of biological signals fail to be properly handled by most statistical methods. We present here a zero-inflated log-normal graphical model (available at https://github.com/vincentprost/Zi-LN) specifically aimed at handling such "biological" zeros, and demonstrate significant performance gains over state-of-the-art statistical methods for the inference of microbial association networks, with most notable gains obtained when analyzing taxonomic profiles displaying sparsity levels on par with real-world metagenomic datasets.

Signal Versus Noise: How to Analyze the Microbiome and Make Progress on Antimicrobial Resistance

Antimicrobial resistance has become a worldwide medical challenge [1], so impactful that vancomycin-resistant Enterococcus (VRE) and methicillin-resistant Staphylococcus aureus (MRSA) have entered the common vernacular. We have attempted to reduce the selective pressure through antimicrobial stewardship, curtail the spread by identifying and isolating carriers and individuals with symptomatic infection, and treat antibiotic-resistant organisms (AROs) by developing novel antimicrobials. Despite these extraordinary measures, the challenge of AROs continues to grow. The gut microbiome, the ecosystem of microbes (ie, the microbiota) and metabolites present upon and within all humans, is an emerging target for both the risk for colonization and defense against infection with AROs. Here, informed from experiences and successes with understanding the role of the microbiome in mediating risk of Clostridioides difficile infection (CDI), we (1) review our understanding of the risk from ARO acquisition; (2) review our current understanding of the gut microbiome's ability to resist colonization with AROs; (3) describe how experimental model systems can test these initial, global insights to arrive at more granular, mechanistic ones; and (4) suggest a path forward to make further progress in the field.

Reduced microbial stability in the active layer is associated with carbon loss under alpine permafrost degradation

Permafrost degradation may induce soil carbon (C) loss, critical for global C cycling, and be mediated by microbes. Despite larger C stored within the active layer of permafrost regions, which are more affected by warming, and the critical roles of Qinghai-Tibet Plateau in C cycling, most previous studies focused on the permafrost layer and in high-latitude areas. We demonstrate in situ that permafrost degradation alters the diversity and potentially decreases the stability of active layer microbial communities. These changes are associated with soil C loss and potentially a positive C feedback. This study provides insights into microbial-mediated mechanisms responsible for C loss within the active layer in degraded permafrost, aiding in the modeling of C emission under future scenarios.

Challenges, Strategies, and Perspectives for Reference-Independent Longitudinal Multi-Omic Microbiome Studies

In recent years, multi-omic studies have enabled resolving community structure and interrogating community function of microbial communities. Simultaneous generation of metagenomic, metatranscriptomic, metaproteomic, and (meta) metabolomic data is more feasible than ever before, thus enabling in-depth assessment of community structure, function, and phenotype, thus resulting in a multitude of multi-omic microbiome datasets and the development of innovative methods to integrate and interrogate those multi-omic datasets. Specifically, the application of reference-independent approaches provides opportunities in identifying novel organisms and functions. At present, most of these large-scale multi-omic datasets stem from spatial sampling (e.g., water/soil microbiomes at several depths, microbiomes in/on different parts of the human anatomy) or case-control studies (e.g., cohorts of human microbiomes). We believe that longitudinal multi-omic microbiome datasets are the logical next step in microbiome studies due to their characteristic advantages in providing a better understanding of community dynamics, including: observation of trends, inference of causality, and ultimately, prediction of community behavior. Furthermore, the acquisition of complementary host-derived omics, environmental measurements, and suitable metadata will further enhance the aforementioned advantages of longitudinal data, which will serve as the basis to resolve drivers of community structure and function to understand the biotic and abiotic factors governing communities and specific populations. Carefully setup future experiments hold great potential to further unveil ecological mechanisms to evolution, microbe-microbe interactions, or microbe-host interactions. In this article, we discuss the challenges, emerging strategies, and best-practices applicable to longitudinal microbiome studies ranging from sampling, biomolecular extraction, systematic multi-omic measurements, reference-independent data integration, modeling, and validation.

Soil microbial interconnections along ecological restoration gradients of lowland forests after slash-and-burn agriculture

Microbial interconnections in soil are pivotal to ecosystem services and restoration. However, little is known about how soil microbial interconnections respond to slash-and-burn agriculture and to the subsequent ecosystem restoration after the practice. Here, we used amplicon sequencing and co-occurrence network analyses to explore the interconnections within soil bacterial and fungal communities in response to slash-and-burn practice and a spontaneous restoration (spanning ca. 60 years) of tropical forests after the practice, in Papua New Guinea. We found significantly higher complexity and greater variations in fungal networks than in those of bacteria, despite no significant changes observed in bacterial or fungal networks across successional stages. Within most successional stages, bacterial core co-occurrences (co-occurrences consistently present across all sub-networks in a stage) were more frequent than those of fungi, indicating higher stability of interconnections between bacteria along succession. The stable interconnections occurred frequently between bacterial taxa (i.e. Sporosarcina, Acidimicrobiale and Bacillaceae) and between ectomycorrhizal fungi (Boletaceae and Russula ochroleuca), implying important ecological roles of these taxa in the ecosystem restoration. Collectively, our results provide new insight into microbial interconnections in response to slash-and-burn agriculture and the subsequent ecosystem restoration, thus promoting a better understanding of microbial roles in ecosystem services and restoration.

Auxiliary rapid identification of pathogenic and antagonistic microorganisms associated with Coptis chinensis root rot by high-throughput sequencing

Root rot reduces the yield and medical quality of C. chinensis (Cc). Previous studies of Coptis root rot focused on the identification of pathogens and the rhizosphere microbial community composition. The present study aimed to identify potential pathogenic and antagonistic microorganisms associated with root rot based on a high-throughput sequencing technique to prevent this disease. Healthy and diseased Cc in the endosphere and rhizosphere from the same field were collected to investigate the differences in microbiome composition and function. The results showed that the composition and function of microbes were different. The numbers of animal pathogens, soil saprotrophs, plant saprotrophs, and wood saprotrophs in the endosphere of diseased Cc were higher than those in the healthy endosphere and were dominated by Phaeosphaeriaceae, Cladorrhinum, Fusarium, Exophiala, and Melanommataceae. Fusarium, Volutella, Cladorrhinum, Cylindrocarpon, and Exophiala were significantly enriched in the endosphere of the diseased plants. Co-occurrence network analysis showed that Bacillus was negatively correlated with Fusarium, Volutella, and Cylindrocarpon, indicating that Bacillus may be antagonistic microorganisms. To verify the sequencing results, F. solani and F. avenaceum were isolated and verified as pathogens, and 14 Bacillus strains were isolated, which displayed an apparent suppression effect against the two pathogens on PDA medium and detached roots. The strategy of high-throughput sequencing has the potential for the comprehensive identification of pathogenic and antagonistic microorganisms for plant disease. These results provide research ideas and microbial resources for future studies on mitigating or preventing root rot damage to Cc.

Whole lung tissue is the preferred sampling method for amplicon-based characterization of murine lung microbiota

BACKGROUND

Low-biomass microbiome studies (such as those of the lungs, placenta, and skin) are vulnerable to contamination and sequencing stochasticity, which obscure legitimate microbial signal. While human lung microbiome studies have rigorously identified sampling strategies that reliably capture microbial signal from these low-biomass microbial communities, the optimal sampling strategy for characterizing murine lung microbiota has not been empirically determined. Performing accurate, reliable characterization of murine lung microbiota and distinguishing true microbial signal from noise in these samples will be critical for further mechanistic microbiome studies in mice.

RESULTS

Using an analytic approach grounded in microbial ecology, we compared bacterial DNA from the lungs of healthy adult mice collected via two common sampling approaches: homogenized whole lung tissue and bronchoalveolar lavage (BAL) fluid. We quantified bacterial DNA using droplet digital PCR, characterized bacterial communities using 16S rRNA gene sequencing, and systematically assessed the quantity and identity of bacterial DNA in both specimen types. We compared bacteria detected in lung specimens to each other and to potential source communities: negative (background) control specimens and paired oral samples. By all measures, whole lung tissue in mice contained greater bacterial signal and less evidence of contamination than did BAL fluid. Relative to BAL fluid, whole lung tissue exhibited a greater quantity of bacterial DNA, distinct community composition, decreased sample-to-sample variation, and greater biological plausibility when compared to potential source communities. In contrast, bacteria detected in BAL fluid were minimally different from those of procedural, reagent, and sequencing controls.

CONCLUSIONS

An ecology-based analytical approach discriminates signal from noise in this low-biomass microbiome study and identifies whole lung tissue as the preferred specimen type for murine lung microbiome studies. Sequencing, analysis, and reporting of potential source communities, including negative control specimens and contiguous biological sites, are crucial for biological interpretation of low-biomass microbiome studies, independent of specimen type. Video abstract.

Network analysis methods for studying microbial communities: A mini review

Microorganisms including bacteria, fungi, viruses, protists and archaea live as communities in complex and contiguous environments. They engage in numerous inter- and intra- kingdom interactions which can be inferred from microbiome profiling data. In particular, network-based approaches have proven helpful in deciphering complex microbial interaction patterns. Here we give an overview of state-of-the-art methods to infer intra-kingdom interactions ranging from simple correlation- to complex conditional dependence-based methods. We highlight common biases encountered in microbial profiles and discuss mitigation strategies employed by different tools and their trade-off with increased computational complexity. Finally, we discuss current limitations that motivate further method development to infer inter-kingdom interactions and to robustly and comprehensively characterize microbial environments in the future.

Archaeal and bacterial communities assembly and co-occurrence networks in subtropical mangrove sediments under Spartina alterniflora invasion

BACKGROUND

Mangrove ecosystems are vulnerable due to the exotic Spartina alterniflora (S. alterniflora) invasion in China. However, little is known about mangrove sediment microbial community assembly processes and interactions under S. alterniflora invasion. Here, we investigated the assembly processes and co-occurrence networks of the archaeal and bacterial communities under S. alterniflora invasion along the coastlines of Fujian province, southeast China.

RESULTS

Assembly of overall archaeal and bacterial communities was driven predominantly by stochastic processes, and the relative role of stochasticity was stronger for bacteria than archaea. Co-occurrence network analyses showed that the network structure of bacteria was more complex than that of the archaea. The keystone taxa often had low relative abundances (conditionally rare taxa), suggesting low abundance taxa may significantly contribute to network stability. Moreover, S. alterniflora invasion increased bacterial and archaeal drift process (part of stochastic processes), and improved archaeal network complexity and stability, but decreased the network complexity and stability of bacteria. This could be attributed to S. alterniflora invasion influenced microbial communities diversity and dispersal ability, as well as soil environmental conditions.

CONCLUSIONS

This study fills a gap in the community assembly and co-occurrence patterns of both archaea and bacteria in mangrove ecosystem under S. alterniflora invasion. Thereby provides new insights of the plant invasion on mangrove microbial biogeographic distribution and co-occurrence network patterns.

Anna Karenina and the subgingival microbiome associated with periodontitis

BACKGROUND

Although localized aggressive periodontitis (LAP), generalized aggressive periodontitis (GAP), and chronic periodontitis (CP) are microbially driven diseases, our inability to separate disease-specific associations from those common to all three forms of periodontitis has hampered biomarker discovery. Therefore, we aimed to map the genomic content of, and the biological pathways encoded by, the microbiomes associated with these clinical phenotypes. We also estimated the extent to which these biomes are governed by the Anna Karenina principle (AKP), which states that eubiotic communities are similar between individuals while disease-associated communities are highly individualized.

METHODS

We collected subgingival plaque from 25 periodontally healthy individuals and diseased sites of 59 subjects with stage 3 periodontitis and used shotgun metagenomics to characterize the aggregate of bacterial genes.

RESULTS

Beta-dispersion metrics demonstrated that AKP was most evident in CP, followed by GAP and LAP. We discovered broad dysbiotic signatures spanning the three phenotypes, with over-representation of pathways that facilitate life in an oxygen-poor, protein- and heme-rich, pro-oxidant environment and enhance capacity for attachment and biofilm formation. Phenotype-specific indicators were more readily evident in LAP microbiome than GAP or CP. Genes that enable acetate-scavenging lifestyle, utilization of alternative nutritional sources, oxidative and nitrosative stress responses, and siderophore production were unique to LAP. An attenuation of virulence-related functionalities and stress response from LAP to GAP to CP was apparent. We also discovered that clinical phenotypes of disease resolved variance in the microbiome with greater clarity than the newly established grading system. Importantly, we observed that one third of the metagenome of LAP is unique to this phenotype while GAP shares significant functional and taxonomic features with both LAP and CP, suggesting either attenuation of an aggressive disease or an early-onset chronic disease.

CONCLUSION

Within the limitations of a small sample size and a cross-sectional study design, the distinctive features of the microbiomes associated with LAP and CP strongly persuade us that these are discrete disease entities, while calling into question whether GAP is a separate disease, or an artifact induced by cross-sectional study designs. Further studies on phenotype-specific microbial genes are warranted to explicate their role in disease etiology. Video Abstract.

Soil depth matters: shift in composition and inter-kingdom co-occurrence patterns of microorganisms in forest soils

Soil depth represents a strong physiochemical gradient that greatly affects soil-dwelling microorganisms. Fungal communities are typically structured by soil depth, but how other microorganisms are structured is less known. Here, we tested whether depth-dependent variation in soil chemistry affects the distribution and co-occurrence patterns of soil microbial communities. This was investigated by DNA metabarcoding in conjunction with network analyses of bacteria, fungi, as well as other micro-eukaryotes, sampled in four different soil depths in Norwegian birch forests. Strong compositional turnover in microbial assemblages with soil depth was detected for all organismal groups. Significantly greater microbial diversity and fungal biomass appeared in the nutrient-rich organic layer, with sharp decrease towards the less nutrient-rich mineral zones. The proportions of copiotrophic bacteria, Arthropoda and Apicomplexa were markedly higher in the organic layer, while patterns were opposite for oligotrophic bacteria, Cercozoa, Ascomycota and ectomycorrhizal fungi. Network analyses indicated more intensive inter-kingdom co-occurrence patterns in the upper mineral layer (0-5 cm) compared to the above organic and the lower mineral soil, signifying substantial influence of soil depth on biotic interactions. This study supports the view that different microbial groups are adapted to different forest soil strata, with varying level of interactions along the depth gradient.

Lake Ecosystem Robustness and Resilience Inferred from a Climate-Stressed Protistan Plankton Network

Network analyses of biological communities allow for identifying potential consequences of climate change on the resilience of ecosystems and their robustness to resist stressors. Using DNA metabarcoding datasets from a three-year-sampling (73 samples), we constructed the protistan plankton co-occurrence network of Lake Zurich, a model lake ecosystem subjected to climate change. Despite several documentations of dramatic lake warming in Lake Zurich, our study provides an unprecedented perspective by linking changes in biotic association patterns to climate stress. Water temperature belonged to the strongest environmental parameters splitting the data into two distinct seasonal networks (October–April; May–September). The expected ecological niche of phytoplankton, weakened through nutrient depletion because of permanent thermal stratification and through parasitic fungi, was occupied by the cyanobacterium Planktothrix rubescens and mixotrophic nanoflagellates. Instead of phytoplankton, bacteria and nanoflagellates were the main prey organisms associated with key predators (ciliates), which contrasts traditional views of biological associations in lake plankton. In a species extinction scenario, the warm season network emerged as more vulnerable than the cold season network, indicating a time-lagged effect of warmer winter temperatures on the communities. We conclude that climate stressors compromise lake ecosystem robustness and resilience through species replacement, richness differences, and succession as indicated by key network properties.

Linking Plant Secondary Metabolites and Plant Microbiomes: A Review

Plant secondary metabolites (PSMs) play many roles including defense against pathogens, pests, and herbivores; response to environmental stresses, and mediating organismal interactions. Similarly, plant microbiomes participate in many of the above-mentioned processes directly or indirectly by regulating plant metabolism. Studies have shown that plants can influence their microbiome by secreting various metabolites and, in turn, the microbiome may also impact the metabolome of the host plant. However, not much is known about the communications between the interacting partners to impact their phenotypic changes. In this article, we review the patterns and potential underlying mechanisms of interactions between PSMs and plant microbiomes. We describe the recent developments in analytical approaches and methods in this field. The applications of these new methods and approaches have increased our understanding of the relationships between PSMs and plant microbiomes. Though the current studies have primarily focused on model organisms, the methods and results obtained so far should help future studies of agriculturally important plants and facilitate the development of methods to manipulate PSMs-microbiome interactions with predictive outcomes for sustainable crop productions.

The microbial network property as a bio-indicator of antibiotic transmission in the environment

Interspecies interaction is an essential mechanism for bacterial communities to develop antibiotic resistance via horizontal gene transfer. Nonetheless, how bacterial interactions vary along the environmental transmission of antibiotics and the underpinnings remain unclear. To address it, we explore potential microbial associations by analyzing bacterial networks generated from 16S rRNA gene sequences and functional networks containing a large number of antibiotic-resistance genes (ARGs). Antibiotic concentration decreased by more than 4000-fold along the environmental transmission chain from manure samples of swine farms to aerobic compost, compost-amended agricultural soils, and neighboring agricultural soils. Both bacterial and functional networks became larger in nodes and links with decreasing antibiotic concentrations, likely resulting from lower antibiotics stress. Nonetheless, bacterial networks became less clustered with decreasing antibiotic concentrations, while functional networks became more clustered. Modularity, a key topological property that enhances system resilience to antibiotic stress, remained high for functional networks, but the modularity values of bacterial networks were the lowest when antibiotic concentrations were intermediate. To explain it, we identified a clear shift from deterministic processes, particularly variable selection, to stochastic processes at intermediate antibiotic concentrations as the dominant mechanism in shaping bacterial communities. Collectively, our results revealed microbial network dynamics and suggest that the modularity value of association networks could serve as an important indicator of antibiotic concentrations in the environment.

Data Analysis Strategies for Microbiome Studies in Human Populations-a Systematic Review of Current Practice

Reproducibility is a major issue in microbiome studies, which is partly caused by missing consensus about data analysis strategies. The complex nature of microbiome data, which are high-dimensional, zero-inflated, and compositional, makes them challenging to analyze, as they often violate assumptions of classic statistical methods. With advances in human microbiome research, research questions and study designs increase in complexity so that more sophisticated data analysis concepts are applied. To improve current practice of the analysis of microbiome studies, it is important to understand what kind of research questions are asked and which tools are used to answer these questions. We conducted a systematic literature review considering all publications focusing on the analysis of human microbiome data from June 2018 to June 2019. Of 1,444 studies screened, 419 fulfilled the inclusion criteria. Information about research questions, study designs, and analysis strategies were extracted. The results confirmed the expected shift to more advanced research questions, as one-third of the studies analyzed clustered data. Although heterogeneity in the methods used was found at any stage of the analysis process, it was largest for differential abundance testing. Especially if the underlying data structure was clustered, we identified a lack of use of methods that appropriately addressed the underlying data structure while taking into account additional dependencies in the data. Our results confirm considerable heterogeneity in analysis strategies among microbiome studies; increasingly complex research questions require better guidance for analysis strategies.

IMPORTANCE The human microbiome has emerged as an important factor in the development of health and disease. Growing interest in this topic has led to an increasing number of studies investigating the human microbiome using high-throughput sequencing methods. However, the development of suitable analytical methods for analyzing microbiome data has not kept pace with the rapid progression in the field. It is crucial to understand current practice to identify the scope for development. Our results highlight the need for an extensive evaluation of the strengths and shortcomings of existing methods in order to guide the choice of proper analysis strategies. We have identified where new methods could be designed to address more advanced research questions while taking into account the complex structure of the data.

kLDM: Inferring Multiple Metagenomic Association Networks based on the Variation of Environmental Factors

Identification of significant biological relationships or patterns is central to many metagenomic studies. Methods that estimate association networks have been proposed for this purpose; however, they assume that associations are static, neglecting the fact that relationships in a microbial ecosystem may vary with changes in environmental factors (EFs), which can result in inaccurate estimations. Therefore, in this study, we propose a computational model, called the k-Lognormal-Dirichlet-Multinomial (kLDM) model, which estimates multiple association networks that correspond to specific environmental conditions, and simultaneously infers microbe–microbe and EF–microbe associations for each network. The effectiveness of the kLDM model was demonstrated on synthetic data, a colorectal cancer (CRC) dataset, the Tara Oceans dataset, and the American Gut Project dataset. The results revealed that the widely-used Spearman’s rank correlation coefficient method performed much worse than the other methods, indicating the importance of separating samples by environmental conditions. Cancer fecal samples were then compared with cancer-free samples, and the estimation achieved by kLDM exhibited fewer associations among microbes but stronger associations between specific bacteria, especially five CRC-associated operational taxonomic units, indicating gut microbe translocation in cancer patients. Some EF-dependent associations were then found within a marine eukaryotic community. Finally, the gut microbial heterogeneity of inflammatory bowel disease patients was detected. These results demonstrate that kLDM can elucidate the complex associations within microbial ecosystems. The kLDM program, R, and Python scripts, together with all experimental datasets, are accessible at https://github.com/tinglab/kLDM.git.

Mechanisms of niche-neutrality balancing can drive the assembling of microbial community

One hotspot of present community ecology is to uncover the mechanisms of community succession. In this study, two popular concepts, niche-neutrality dynamic balancing and co-occurrence network analysis, were integrated to investigate the dispersal dynamics of microbial communities in a freshwater river continuum in subtropical China. Results showed that when habitat conditions were mild and appropriate, such as in the clean upstream river, free of heavy pollution or long-lasting extreme disturbances, stochastic processes could increase species diversities, and organize communities into relatively loosely linked and stable networks with higher modularity and more modules. However, when conditions became degraded under heavy pollution, the influence of neutrality diminished, and niche-based selection imposed more constraints on communities and guided the assembling processes in certain directions: depleting species richness, strengthening interspecies connections and breaking boundaries of modules. Consequently, communities became more sensitive to fluctuations so as to deal with the harsh conditions efficiently. Another interesting finding was that, both as keystone taxa of communities, module hubs were mostly neutrally distributed generalists with high abundances, and were beneficial to many related operational taxonomic units. In contrast, connectors were less abundant and their distributions were more subjected to the environments. Therefore, connectors were probably responsible for the information transmission between microbial communities and environments, as well as between different modules, and thus could restrict the dispersal of microbes and guide the direction of community assembly.

Guild-based analysis for understanding gut microbiome in human health and diseases

To demonstrate the causative role of gut microbiome in human health and diseases, we first need to identify, via next-generation sequencing, potentially important functional members associated with specific health outcomes and disease phenotypes. However, due to the strain-level genetic complexity of the gut microbiota, microbiome datasets are highly dimensional and highly sparse in nature, making it challenging to identify putative causative agents of a particular disease phenotype. Members of an ecosystem seldomly live independently from each other. Instead, they develop local interactions and form inter-member organizations to influence the ecosystem's higher-level patterns and functions. In the ecological study of macro-organisms, members are defined as belonging to the same "guild" if they exploit the same class of resources in a similar way or work together as a coherent functional group. Translating the concept of "guild" to the study of gut microbiota, we redefine guild as a group of bacteria that show consistent co-abundant behavior and likely to work together to contribute to the same ecological function. In this opinion article, we discuss how to use guilds as the aggregation unit to reduce dimensionality and sparsity in microbiome-wide association studies for identifying candidate gut bacteria that may causatively contribute to human health and diseases.

Japanese quail (Coturnix japonica) as a novel model to study the relationship between the avian microbiome and microbial endocrinology-based host-microbe interactions

BACKGROUND

Microbial endocrinology, which is the study of neuroendocrine-based interkingdom signaling, provides a causal mechanistic framework for understanding the bi-directional crosstalk between the host and microbiome, especially as regards the effect of stress on health and disease. The importance of the cecal microbiome in avian health is well-recognized, yet little is understood regarding the mechanisms underpinning the avian host-microbiome relationship. Neuroendocrine plasticity of avian tissues that are focal points of host-microbiome interaction, such as the gut and lung, has likewise received limited attention. Avian in vivo models that enable the study of the neuroendocrine dynamic between host and microbiome are needed. As such, we utilized Japanese quail (Coturnix japonica) that diverge in corticosterone response to stress to examine the relationship between stress-related neurochemical concentrations at sites of host-microbe interaction, such as the gut, and the cecal microbiome.

RESULTS

Our results demonstrate that birds which contrast in corticosterone response to stress show profound separation in cecal microbial community structure as well as exhibit differences in tissue neurochemical concentrations and structural morphologies of the gut. Changes in neurochemicals known to be affected by the microbiome were also identified in tissues outside of the gut, suggesting a potential relationship in birds between the cecal microbiome and overall avian physiology.

CONCLUSIONS

The present study provides the first evidence that the structure of the avian cecal microbial community is shaped by selection pressure on the bird for neuroendocrine response to stress. Identification of unique region-dependent neurochemical changes in the intestinal tract following stress highlights environmental stressors as potential drivers of microbial endocrinology-based mechanisms of avian host-microbiome dialogue. Together, these results demonstrate that tissue neurochemical concentrations in the avian gut may be related to the cecal microbiome and reveal the Japanese quail as a novel avian model in which to further examine the mechanisms underpinning these relationships. Video abstract.

Niche Differentiation of Comammox Nitrospira in the Mudflat and Reclaimed Agricultural Soils Along the North Branch of Yangtze River Estuary

The discovery of complete ammonia oxidation (comammox), oxidizing ammonia to nitrate via nitrite in a single organism, has redefined the traditional recognition of the two-step nitrification driven by two functional groups (ammonia-oxidizing and nitrite-oxidizing microorganisms). However, the understanding of the distribution and niche differentiation of comammox Nitrospira in the estuarine mudflats and their reclaimed agricultural soils is still limited. Here, we investigated the abundance, diversity and community structures of comammox Nitrospira in the mudflats and the reclaimed agricultural soils in the northern Yangtze River estuary. Quantitative PCR showed the abundances of amoA genes of comammox were lower than that of ammonia-oxidizing bacteria (AOB) in nearly all samples. Amplicon sequencing of amoA genes revealed that the community structures of comammox Nitrospira were significantly (P < 0.001) different between the original mudflats and the reclaimed agricultural soils, indicating niche differentiation among comammox Nitrospira clades (clade A.1, clade A.2, and clade B). The clade A.1 was the dominant group of comammox Nitrospira in the mudflats, while clade B predominated in the agricultural soils. However, the members of clade A.2 could be clearly divided into two groups, the mudflat-preferred and agricultural soil-preferred groups, suggesting more complicated ecological preferences within this sub-clade. Furthermore, it was demonstrated that salinity, organic matter (OM) and NO₃^–-N had a significantly influence on the distribution of comammox Nitrospira in the estuarine environment. Clade A.1 and nearly half members of clade A.2 were positively correlated with salinity, and negatively correlated with the concentrations of OM and NO₃^–-N. In contrast, the clade B and the other half members of clade A.2 showed the exact opposite pattern: a negative correlation with salinity and positive correlation with OM and NO₃^–-N. The co-occurrence network demonstrated that the operational taxonomic units (OTUs) within the same (sub-)clade were mostly positively correlated, indicating the similar niche preferences among the members from the same (sub-)clade of comammox Nitrospira. Taken together, our results revealed the niche differentiation of comammox Nitrospira in estuarine ecosystems where salinity and OM were the primary factors responsible for the distinct ecological distribution patterns.

Relationship between dental and periodontal health status and the salivary microbiome: bacterial diversity, co-occurrence networks and predictive models

The present study used 16S rRNA gene amplicon sequencing to assess the impact on salivary microbiome of different grades of dental and periodontal disease and the combination of both (hereinafter referred to as oral disease), in terms of bacterial diversity, co-occurrence network patterns and predictive models. Our scale of overall oral health was used to produce a convenience sample of 81 patients from 270 who were initially recruited. Saliva samples were collected from each participant. Sequencing was performed in Illumina MiSeq with 2 × 300 bp reads, while the raw reads were processed according to the Mothur pipeline. The statistical analysis of the 16S rDNA sequencing data at the species level was conducted using the phyloseq, DESeq2, Microbiome, SpiecEasi, igraph, MixOmics packages. The simultaneous presence of dental and periodontal pathology has a potentiating effect on the richness and diversity of the salivary microbiota. The structure of the bacterial community in oral health differs from that present in dental, periodontal or oral disease, especially in high grades. Supragingival dental parameters influence the microbiota’s abundance more than subgingival periodontal parameters, with the former making a greater contribution to the impact that oral health has on the salivary microbiome. The possible keystone OTUs are different in the oral health and disease, and even these vary between dental and periodontal disease: half of them belongs to the core microbiome and are independent of the abundance parameters. The salivary microbiome, involving a considerable number of OTUs, shows an excellent discriminatory potential for distinguishing different grades of dental, periodontal or oral disease; considering the number of predictive OTUs, the best model is that which predicts the combined dental and periodontal status.

The yellow perch (Perca flavescens) microbiome revealed resistance to colonisation mostly associated with neutralism driven by rare taxa under cadmium disturbance

Background

Disentangling the dynamics of microbial interactions within communities improves our comprehension of metacommunity assembly of microbiota during host development and under perturbations. To assess the impact of stochastic variation of neutral processes on microbiota structure and composition under disturbance, two types of microbial habitats, free-living (water), and host-associated (skin and gut) were experimentally exposed to either a constant or gradual selection regime exerted by two sublethal cadmium chloride dosages (CdCl₂). Yellow Perch (Perca flavescens) was used as a piscivorous ecotoxicological model. Using 16S rDNA gene based metataxonomics, quantitative diversity metrics of water, skin and gut microbial communities were characterized along with development and across experimental conditions.

Results

After 30 days, constant and gradual selection regimes drove a significant alpha diversity increase for both skin and gut microbiota. In the skin, pervasive negative correlations between taxa in both selection regimes in addition to the taxonomic convergence with the environmental bacterial community, suggest a loss of colonisation resistance resulting in the dysbiosis of yellow perch microbiota. Furthermore, the network connectivity in gut microbiome was exclusively maintained by rare (low abundance) OTUs, while most abundant OTUs were mainly composed of opportunistic invaders such as Mycoplasma and other genera related to fish pathogens such as Flavobacterium. Finally, the mathematical modelling of community assembly using both non-linear least squares models (NLS) based estimates of migration rates and normalized stochasticity ratios (NST) based beta-diversity distances suggested neutral processes drove by taxonomic drift in host and water communities for almost all treatments. The NLS models predicted higher demographic stochasticity in the cadmium-free host and water microbiomes, however, NST models suggested higher ecological stochasticity under perturbations.

Conclusions

Neutral models agree that water and host-microbiota assembly promoted by rare taxa have evolved predominantly under neutral processes with potential involvement of deterministic forces sourced from host filtering and cadmium selection. The early signals of perturbations in the skin microbiome revealed antagonistic interactions by a preponderance of negative correlations in the co-abundance networks. Our findings enhance our understanding of community assembly host-associated and free-living under anthropogenic selective pressure.

Hidden heterogeneity and co-occurrence networks of soil prokaryotic communities revealed at the scale of individual soil aggregates

Sequencing PCR‐amplified gene fragments from metagenomic DNA is a widely applied method for studying the diversity and dynamics of soil microbial communities. Typically, DNA is extracted from 0.25 to 1 g of soil. These amounts, however, neglect the heterogeneity of soil present at the scale of soil aggregates and thus ignore a crucial scale for understanding the structure and functionality of soil microbial communities. Here, we show with a nitrogen‐depleted agricultural soil the impact of reducing the amount of soil used for DNA extraction from 250 mg to approx. 1 mg to access spatial information on the prokaryotic community structure, as indicated by 16S rRNA gene amplicon analyses. Furthermore, we demonstrate that individual aggregates from the same soil differ in their prokaryotic community compositions. The analysis of 16S rRNA gene amplicon sequences from individual soil aggregates allowed us, in contrast to 250 mg soil samples, to construct a co‐occurrence network that provides insight into the structure of microbial associations in the studied soil. Two dense clusters were apparent in the network, one dominated by Thaumarchaeota, known to be capable of ammonium oxidation at low N concentrations, and the other by Acidobacteria subgroup 6, representing an oligotrophic lifestyle to obtain energy from SOC. Overall this study demonstrates that DNA obtained from individual soil aggregates provides new insights into how microbial communities are assembled.

Aquatic food webs in deep temperate lakes: Key species establish through their autecological versatility

Microbial planktonic communities are the basis of food webs in aquatic ecosystems since they contribute substantially to primary production and nutrient recycling. Network analyses of DNA metabarcoding data sets emerged as a powerful tool to untangle the complex ecological relationships among the key players in food webs. In this study, we evaluated co-occurrence networks constructed from time-series metabarcoding data sets (12 months, biweekly sampling) of protistan plankton communities in surface layers (epilimnion) and bottom waters (hypolimnion) of two temperate deep lakes, Lake Mondsee (Austria) and Lake Zurich (Switzerland). Lake Zurich plankton communities were less tightly connected, more fragmented and had a higher susceptibility to a species extinction scenario compared to Lake Mondsee communities. We interpret these results as a lower robustness of Lake Zurich protistan plankton to environmental stressors, especially stressors resulting from climate change. In all networks, the phylum Ciliophora contributed the highest number of nodes, among them several in key positions of the networks. Associations in ciliate-specific subnetworks resembled autecological species-specific traits that indicate adaptions to specific environmental conditions. We demonstrate the strength of co-occurrence network analyses to deepen our understanding of plankton community dynamics in lakes and indicate biotic relationships, which resulted in new hypotheses that may guide future research in climate-stressed ecosystems.

Microbial networks inferred from environmental DNA data for biomonitoring ecosystem change: Strengths and pitfalls

Environmental DNA contains information on the species interaction networks that support ecosystem functions and services. Next-generation biomonitoring proposes the use of this data to reconstruct ecological networks in real time and then compute network-level properties to assess ecosystem change. We investigated the relevance of this proposal by assessing: (i) the replicability of DNA-based networks in the absence of ecosystem change, and (ii) the benefits and shortcomings of community- and network-level properties for monitoring change. We selected crop-associated microbial networks as a case study because they support disease regulation services in agroecosystems and analysed their response to change in agricultural practice between organic and conventional systems. Using two statistical methods of network inference, we showed that network-level properties, especially β-properties, could detect change. Moreover, consensus networks revealed robust signals of interactions between the most abundant species, which differed between agricultural systems. These findings complemented those obtained with community-level data that showed, in particular, a greater microbial diversity in the organic system. The limitations of network-level data included (i) the very high variability of network replicates within each system; (ii) the low number of network replicates per system, due to the large number of samples needed to build each network; and (iii) the difficulty in interpreting links of inferred networks. Tools and frameworks developed over the last decade to infer and compare microbial networks are therefore relevant to biomonitoring, provided that the DNA metabarcoding data sets are large enough to build many network replicates and progress is made to increase network replicability and interpretation.

Measuring the microbiome: Best practices for developing and benchmarking microbiomics methods

Microbiomes are integral components of diverse ecosystems, and increasingly recognized for their roles in the health of humans, animals, plants, and other hosts. Given their complexity (both in composition and function), the effective study of microbiomes (microbiomics) relies on the development, optimization, and validation of computational methods for analyzing microbial datasets, such as from marker-gene (e.g., 16S rRNA gene) and metagenome data. This review describes best practices for benchmarking and implementing computational methods (and software) for studying microbiomes, with particular focus on unique characteristics of microbiomes and microbiomics data that should be taken into account when designing and testing microbiomics methods.

Environmental heterogeneity determines the ecological processes that govern bacterial metacommunity assembly in a floodplain river system

How diversity is structured has been a central goal of microbial ecology. In freshwater ecosystems, selection has been found to be the main driver shaping bacterial communities. However, its relative importance compared with other processes (dispersal, drift, diversification) may depend on spatial heterogeneity and the dispersal rates within a metacommunity. Still, a decrease in the role of selection is expected with increasing dispersal homogenization. Here, we investigate the main ecological processes modulating bacterial assembly in contrasting scenarios of environmental heterogeneity. We carried out a spatiotemporal survey in the floodplain system of the Paraná River. The bacterioplankton metacommunity was studied using both statistical inferences based on phylogenetic and taxa turnover as well as co-occurrence networks. We found that selection was the main process determining community assembly even at both extremes of environmental heterogeneity and homogeneity, challenging the general view that the strength of selection is weakened due to dispersal homogenization. The ecological processes acting on the community also determined the connectedness of bacterial networks associations. Heterogeneous selection promoted more interconnected networks increasing β-diversity. Finally, spatiotemporal heterogeneity was an important factor determining the number and identity of the most highly connected taxa in the system. Integrating all these empirical evidences, we propose a new conceptual model that elucidates how the environmental heterogeneity determines the action of the ecological processes shaping the bacterial metacommunity.

Shrinkage improves estimation of microbial associations under different normalization methods

Estimation of statistical associations in microbial genomic survey count data is fundamental to microbiome research. Experimental limitations, including count compositionality, low sample sizes and technical variability, obstruct standard application of association measures and require data normalization prior to statistical estimation. Here, we investigate the interplay between data normalization, microbial association estimation and available sample size by leveraging the large-scale American Gut Project (AGP) survey data. We analyze the statistical properties of two prominent linear association estimators, correlation and proportionality, under different sample scenarios and data normalization schemes, including RNA-seq analysis workflows and log-ratio transformations. We show that shrinkage estimation, a standard statistical regularization technique, can universally improve the quality of taxon-taxon association estimates for microbiome data. We find that large-scale association patterns in the AGP data can be grouped into five normalization-dependent classes. Using microbial association network construction and clustering as downstream data analysis examples, we show that variance-stabilizing and log-ratio approaches enable the most taxonomically and structurally coherent estimates. Taken together, the findings from our reproducible analysis workflow have important implications for microbiome studies in multiple stages of analysis, particularly when only small sample sizes are available.

Anti-Tick Microbiota Vaccine Impacts Ixodes ricinus Performance during Feeding

The tick microbiota is a highly complex ensemble of interacting microorganisms. Keystone taxa, with a central role in the microbial networks, support the stability and fitness of the microbial communities. The keystoneness of taxa in the tick microbiota can be inferred from microbial co-occurrence networks. Microbes with high centrality indexes are highly connected with other taxa of the microbiota and are expected to provide important resources to the microbial community and/or the tick. We reasoned that disturbance of vector microbiota by removal of ubiquitous and abundant keystone bacteria may disrupt the tick-microbiota homeostasis causing harm to the tick host. These observations and reasoning prompted us to test the hypothesis that antibodies targeting keystone bacteria may harm the ticks during feeding on immunized hosts. To this aim, in silico analyses were conducted to identify keystone bacteria in the microbiota of Ixodes nymphs. The family Enterobacteriaceae was among the top keystone taxa identified in Ixodes microbiota. Immunization of α-1,3-galactosyltransferase-deficient-C57BL/6 (α1,3GT KO) mice with a live vaccine containing the Enterobacteriaceae bacterium Escherichia coli strain BL21 revealed that the production of anti-E. coli and anti-α-Gal IgM and IgG was associated with high mortality of I. ricinus nymphs during feeding. However, this effect was absent in two different strains of wild type mice, BALB/c and C57BL/6. This result concurred with a wide distribution of α-1,3-galactosyltransferase genes, and possibly α-Gal, in Enterobacteriaceae and other bacteria of tick microbiota. Interestingly, the weight of I. ricinus nymphs that fed on E. coli-immunized C57BL/6 was significantly higher than the weight of ticks that fed on C57BL/6 immunized with a mock vaccine. Our results suggest that anti-tick microbiota vaccines are a promising tool for the experimental manipulation of vector microbiota, and potentially the control of ticks and tick-borne pathogens.

Resource availability drives bacterial succession during leaf-litter decomposition in a bromeliad ecosystem

Despite the growing number of investigations on microbial succession during the last decade, most of our knowledge on primary succession of bacteria in natural environments comes from conceptual models and/or studies of chronosequences. Successional patterns of litter-degrading bacteria remain poorly documented, especially in undisturbed environments. Here we conducted an experiment with tank bromeliads as natural freshwater microcosms to assess major trends in bacterial succession on two leaf-litter species incubated with or without animal exclusion. We used amplicon sequencing and a co-occurrence network to assess changes in bacterial community structure according to treatments. Alpha-diversity and community complexity displayed the same trends regardless of the treatments, highlighting that primary succession of detrital-bacteria is subject to resource limitation and biological interactions, much like macro-organisms. Shifts in bacterial assemblages along the succession were characterized by an increase in uncharacterized taxa and potential N-fixing bacteria, the latter being involved in positive co-occurrence between taxa. These findings support the hypothesis of interdependence between taxa as a significant niche-based process shaping bacterial communities during the advanced stage of succession.

Unravelling the importance of the eukaryotic and bacterial communities and their relationship with Legionella spp. ecology in cooling towers: a complex network

BACKGROUND

Cooling towers are a major source of large community-associated outbreaks of Legionnaires' disease, a severe pneumonia. This disease is contracted when inhaling aerosols that are contaminated with bacteria from the genus Legionella, most importantly Legionella pneumophila. How cooling towers support the growth of this bacterium is still not well understood. As Legionella species are intracellular parasites of protozoa, it is assumed that protozoan community in cooling towers play an important role in Legionella ecology and outbreaks. However, the exact mechanism of how the eukaryotic community contributes to Legionella ecology is still unclear. Therefore, we used 18S rRNA gene amplicon sequencing to characterize the eukaryotic communities of 18 different cooling towers. The data from the eukaryotic community was then analysed with the bacterial community of the same towers in order to understand how each community could affect Legionella spp. ecology in cooling towers.

RESULTS

We identified several microbial groups in the cooling tower ecosystem associated with Legionella spp. that suggest the presence of a microbial loop in these systems. Dissolved organic carbon was shown to be a major factor in shaping the eukaryotic community and may be an important factor for Legionella ecology. Network analysis, based on co-occurrence, revealed that Legionella was correlated with a number of different organisms. Out of these, the bacterial genus Brevundimonas and the ciliate class Oligohymenophorea were shown, through in vitro experiments, to stimulate the growth of L. pneumophila through direct and indirect mechanisms.

CONCLUSION

Our results suggest that Legionella ecology depends on the host community, including ciliates and on several groups of organisms that contribute to its survival and growth in the cooling tower ecosystem. These findings further support the idea that some cooling tower microbiomes may promote the survival and growth of Legionella better than others. Video Abstract.

A network-based approach to deciphering a dynamic microbiome's response to a subtle perturbation

Over the past decades, one main issue that has emerged in ecological and environmental research is how losses in biodiversity influence ecosystem dynamics and functioning, and consequently human society. Although biodiversity is a common indicator of ecosystem functioning, it is difficult to measure biodiversity in microbial communities exposed to subtle or chronic environmental perturbations. Consequently, there is a need for alternative bioindicators to detect, measure, and monitor gradual changes in microbial communities against these slight, chronic, and continuous perturbations. In this study, microbial networks before and after subtle perturbations by adding S. acidaminiphila showed diverse topological niches and 4-node motifs in which microbes with co-occurrence patterns played the central roles in regulating and adjusting the intertwined relationships among microorganisms in response to the subtle environmental changes. This study demonstrates that microbial networks are a good bioindicator for chronic perturbation and should be applied in a variety of ecological investigations.

Spatiotemporal Distribution of the Environmental Microbiota in Food Processing Plants as Impacted by Cleaning and Sanitizing Procedures: the Case of Slaughterhouses and Gaseous Ozone

Microbial complexity and contamination levels in food processing plants heavily impact the final product fate and are mainly controlled by proper environmental cleaning and sanitizing. Among the emerging disinfection technologies, ozonation is considered an effective strategy to improve the ordinary cleaning and sanitizing of slaughterhouses. However, its effects on contamination levels and environmental microbiota still need to be understood. For this purpose, we monitored the changes in microbiota composition in different slaughterhouse environments during the phases of cleaning/sanitizing and ozonation at 40, 20, or 4 ppm. Overall, the meat processing plant microbiota differed significantly between secondary processing rooms and deboning rooms, with a greater presence of psychrotrophic taxa in secondary processing rooms because of their lower temperatures. Cleaning/sanitizing procedures significantly reduced the contamination levels and in parallel increased the number of detectable operational taxonomic units (OTUs), by removing the masking effect of the most abundant human/animal-derived OTUs, which belonged to the phylum Firmicutes Subsequently, ozonation at 40 or 20 ppm effectively decreased the remaining viable bacterial populations. However, we could observe selective ozone-mediated inactivation of psychrotrophic bacteria only in the secondary processing rooms. There, the Brochothrix and Pseudomonas abundances and their viable counts were significantly affected by 40 or 20 ppm of ozone, while more ubiquitous genera like Staphylococcus showed a remarkable resistance to the same treatments. This study showed the effectiveness of highly concentrated gaseous ozone as an adjunct sanitizing method that can minimize cross-contamination and so extend the meat shelf life.IMPORTANCE Our in situ survey demonstrates that RNA-based sequencing of 16S rRNA amplicons is a reliable approach to qualitatively probe, at high taxonomic resolution, the changes triggered by new and existing cleaning/sanitizing strategies in the environmental microbiota in human-built environments. This approach could soon represent a fast tool to clearly define which routine sanitizing interventions are more suitable for a specific food processing environment, thus limiting the costs of special cleaning interventions and potential product loss.

Association of Body Mass Index with Fecal Microbial Diversity and Metabolites in the Northern Finland Birth Cohort

BACKGROUND

Obesity is an established risk factor for multiple cancer types. Lower microbial richness has been linked to obesity, but human studies are inconsistent, and associations of early-life body mass index (BMI) with the fecal microbiome and metabolome are unknown.

METHODS

We characterized the fecal microbiome (n = 563) and metabolome (n = 340) in the Northern Finland Birth Cohort 1966 using 16S rRNA gene sequencing and untargeted metabolomics. We estimated associations of adult BMI and BMI history with microbial features and metabolites using linear regression and Spearman correlations (rs ) and computed correlations between bacterial sequence variants and metabolites overall and by BMI category.

RESULTS

Microbial richness, including the number of sequence variants (rs = -0.21, P < 0.0001), decreased with increasing adult BMI but was not independently associated with BMI history. Adult BMI was associated with 56 metabolites but no bacterial genera. Significant correlations were observed between microbes in 5 bacterial phyla, including 18 bacterial genera, and metabolites in 49 of the 62 metabolic pathways evaluated. The genera with the strongest correlations with relative metabolite levels (positively and negatively) were Blautia, Oscillospira, and Ruminococcus in the Firmicutes phylum, but associations varied by adult BMI category.

CONCLUSIONS

BMI is strongly related to fecal metabolite levels, and numerous associations between fecal microbial features and metabolite levels underscore the dynamic role of the gut microbiota in metabolism.

IMPACT

Characterizing the associations between the fecal microbiome, the fecal metabolome, and BMI, both recent and early-life exposures, provides critical background information for future research on cancer prevention and etiology.

Direct interaction network inference for compositional data via codaloss

16S rRNA gene sequencing and whole microbiome sequencing make it possible and stable to quantitatively analyze the composition of microbial communities and the relationship among microbial communities, microbes, and hosts. One essential step in the analysis of microbiome compositional data is inferring the direct interaction network among microbial species, bringing to light the potential underlying mechanism that regulates interaction in their communities. However, standard statistical analysis may obtain spurious results due to compositional nature of microbiome data; therefore, network recovery of microbial communities remains challenging. Here, we propose a novel loss function called codaloss for direct microbes interaction network estimation under the sparsity assumptions. We develop an alternating direction optimization algorithm to obtain sparse solution of codaloss as estimator. Compared to other state-of-the-art methods, our model makes less assumptions about the microbial networks. The simulation and real microbiome data results show that our method outperforms other methods in network inference. An implementation of codaloss is available from https://github.com/xuebaliang/Codaloss.

Covariation patterns of phytoplankton and bacterioplankton in hypertrophic shallow lakes

The aim of this work was to assess the temporal patterns in the community composition of phytoplankton (PCC) and bacterioplankton (BCC) in two interconnected and hypertrophic Pampean shallow lakes in Argentina. Factors shaping their community dynamics and community temporal covariations were also analysed. We performed 4 years of seasonal samplings (2012-2016) and communities were studied by the Utermöhl approach (PCC) and Illumina MiSeq sequencing (BCC). We found marked seasonal variations in both communities and inter-annual variations with decreasing microbial community similarities during the study. We also observed covariation in community-level dynamics among PCC and BCC within and between shallow lakes. The within-lake covariations remained positive and significant, while controlling for the effects of intrinsic (environmental) and extrinsic (temporal and meteorological) factors, suggesting a community coupling mediated by intrinsic biotic interactions. Algal-bacterial associations between different taxa of phytoplankton and bacterioplankton within each lake were also found. PCC was mainly explained by pure regional extrinsic (17-21%) and intrinsic environmental (8-9%) factors, while BCC was explained by environmental (8-10%) and biotic interactions with phytoplankton (7-8%). Our results reveal that the influence of extrinsic regional factors can be channeled to bacterioplankton through both environmental (i.e. water temperature) and phytoplankton effects.

Disentangling the seasonal co-occurrence patterns and ecological stochasticity of planktonic and benthic bacterial communities within multiple lakes

Both the planktonic bacterial community (PBC) and benthic bacterial community (BBC) are important for biogeochemical processes in freshwater lakes. Despite their ecological significance, little is known about their seasonal co-occurrence patterns and the ecological processes that drive them. In this study, we aimed to investigate the ecological associations among bacterial taxa and assembly processes of PBC and BBC in different seasons. We used 16S rRNA gene high-throughput sequencing of a total of 150 water and sediment samples collected from multiple lakes distributed in an urban region of China during winter and summer. Our results revealed that PBC showed stronger seasonal variations in co-occurrence patterns than BBC, suggesting that BBC had greater temporal stability than PBC. Winter PBC network was characterized by higher connectivity and complexity, and thereby the formation of a highly stable community structure; whereas lower connectivity arising from the presence of fewer predicted keystone taxa (hubs and connectors in a network) was destabilizing to summer PBC network. In addition, the phylum Firmicutes identified as a putative keystone taxon of PBC in both seasons played a non-negligible role in maintaining network structure which may result from strong functional associations with other bacterioplankton. Temperature and pH were the best explanatory factors predicting the seasonal co-occurrence patterns of PBC and BBC, respectively. Normalized stochasticity ratio based on null-model analysis indicated that deterministic processes overwhelmed stochastic processes in governing the assembly of PBC and BBC in both seasons. However, we observed a greater influence of ecological stochasticity on BBC assembly than PBC assembly in both seasons. Taken together, these findings provide insights into understanding the impacts of habitat heterogeneity and seasonal variability on microbial assemblage patterns in lake ecosystems.

Environmental and Microbial Interactions Shape Methane-Oxidizing Bacterial Communities in a Stratified Lake

In stratified lakes, methane-oxidizing bacteria (MOB) are strongly mitigating methane fluxes to the atmosphere by consuming methane entering the water column from the sediments. MOB communities in lakes are diverse and vertically structured, but their spatio-temporal dynamics along the water column as well as physico-chemical parameters and interactions with other bacterial species that drive the community assembly have so far not been explored in depth. Here, we present a detailed investigation of the MOB and bacterial community composition and a large set of physico-chemical parameters in a shallow, seasonally stratified, and sub-alpine lake. Four highly resolved vertical profiles were sampled in three different years and during various stages of development of the stratified water column. Non-randomly assembled MOB communities were detected in all compartments. We could identify methane and oxygen gradients and physico-chemical parameters like pH, light, available copper and iron, and total dissolved nitrogen as important drivers of the MOB community structure. In addition, MOB were well-integrated into a bacterial-environmental network. Partial redundancy analysis of the relevance network of physico-chemical variables and bacteria explained up to 84% of the MOB abundances. Spatio-temporal MOB community changes were 51% congruent with shifts in the total bacterial community and 22% of variance in MOB abundances could be explained exclusively by the bacterial community composition. Our results show that microbial interactions may play an important role in structuring the MOB community along the depth gradient of stratified lakes.

A network approach to investigating the key microbes and stability of gut microbial communities in a mouse neuropathic pain model

Background

Neuropathic pain is an abnormally increased sensitivity to pain, especially from mechanical or thermal stimuli. To date, the current pharmacological treatments for neuropathic pain are still unsatisfactory. The gut microbiota reportedly plays important roles in inducing neuropathic pain, so probiotics have also been used to treat it. However, the underlying questions around the interactions in and stability of the gut microbiota in a spared nerve injury-induced neuropathic pain model and the key microbes (i.e., the microbes that play critical roles) involved have not been answered. We collected 66 fecal samples over 2 weeks (three mice and 11 time points in spared nerve injury-induced neuropathic pain and Sham groups). The 16S rRNA gene was polymerase chain reaction amplified, sequenced on a MiSeq platform, and analyzed using a MOTHUR- UPARSE pipeline.

Results

Here we show that spared nerve injury-induced neuropathic pain alters gut microbial diversity in mice. We successfully constructed reliable microbial interaction networks using the Metagenomic Microbial Interaction Simulator (MetaMIS) and analyzed these networks based on 177,147 simulations. Interestingly, at a higher resolution, our results showed that spared nerve injury-induced neuropathic pain altered both the stability of the microbial community and the key microbes in a gut micro-ecosystem. Oscillospira, which was classified as a low-abundance and core microbe, was identified as the key microbe in the Sham group, whereas Staphylococcus, classified as a rare and non-core microbe, was identified as the key microbe in the spared nerve injury-induced neuropathic pain group.

Conclusions

In summary, our results provide novel experimental evidence that spared nerve injury-induced neuropathic pain reshapes gut microbial diversity, and alters the stability and key microbes in the gut.

Microbial co-occurrence networks of gut microbiota reveal community conservation and diet-associated shifts in cichlid fishes

Background

The extent to which deterministic rather than stochastic processes guide gut bacteria co-existence and ultimately their assembling into a community remains largely unknown. Co-occurrence networks of bacterial associations offer a powerful approach to begin exploring gut microbial community structure, maintenance and dynamics, beyond compositional aspects alone. Here we used an iconic model system, the cichlid fishes, with their multiple lake assemblages and extraordinary ecological diversity, to investigate a) patterns of microbial associations that were robust to major phylogeographical variables, and b) changes in microbial network structure along dietary shifts. We tackled these objectives using the large gut microbiota sequencing dataset available (nine lakes from Africa and America), building geographical and diet-specific networks and performing comparative network analyses.

Results

Major findings indicated that lake and continental microbial networks were highly resembling in global topology and node taxonomic composition, despite the heterogeneity of the samples. A small fraction of the observed co-occurrences among operational taxonomic units (OTUs) was conserved across all lake assemblages. These were all positive associations and involved OTUs within the genera Cetobacterium and Turicibacter and several OTUs belonging to the families of Peptostreptococcaceae and Clostridiaceae (order Clostridiales). Mapping of diet contribution on the African Lake Tanganyika network (therefore excluding the geographic variable) revealed a clear community change from carnivores (C) to omnivores (O) to herbivores (H). Node abundances and effect size for pairwise comparisons between diets supported a strong contrasting pattern between C and H. Moreover, diet-associated nodes in H formed complex modules of positive interactions among taxonomically diverse bacteria (mostly Verrucomicrobia and Proteobacteria).

Conclusions

Conservation of microbial network topologies and specific bacterial associations across distinct lake assemblages point to a major host-associated effect and potential deterministic processes shaping the cichlid gut microbiota. While the origin and biological relevance of these common associations remain unclear, their persistence suggests an important functional role in the cichlid gut. Among the very diverse cichlids of L. Tanganyika, diet nonetheless represents a major driver of microbial community changes. By intersecting results from predictive network inferences and experimental trials, future studies will be directed to explore the strength of these associations, predict the outcome of community alterations driven by diet and ultimately help understanding the role of gut microbiota in cichlid trophic diversification.

Streamlined and Abundant Bacterioplankton Thrive in Functional Cohorts

This study examines evolutionary and ecological relationships of three of the most ubiquitous and abundant freshwater bacterial genera: “Ca. Planktophila” (acI-A), “Ca. Nanopelagicus” (acI-B), and “Ca. Fonsibacter” (LD12). Due to high abundance, these genera might have a significant influence on nutrient cycling in freshwaters worldwide, and this study adds a layer of understanding to how seemingly competing clades of bacteria can coexist by having different cooperation strategies. Our synthesis ties together network and ecological theory with empirical evidence and lays out a framework for how the functioning of populations within complex microbial communities can be studied.

While fastidious microbes can be abundant and ubiquitous in their natural communities, many fail to grow axenically in laboratories due to auxotrophies or other dependencies. To overcome auxotrophies, these microbes rely on their surrounding cohort. A cohort may consist of kin (ecotypes) or more distantly related organisms (community) with the cooperation being reciprocal or nonreciprocal and expensive (Black Queen hypothesis) or costless (by-product). These metabolic partnerships (whether at single species population or community level) enable dominance by and coexistence of these lineages in nature. Here we examine the relevance of these cooperation models to explain the abundance and ubiquity of the dominant fastidious bacterioplankton of a dimictic mesotrophic freshwater lake. Using both culture-dependent (dilution mixed cultures) and culture-independent (small subunit [SSU] rRNA gene time series and environmental metagenomics) methods, we independently identified the primary cohorts of actinobacterial genera “Candidatus Planktophila” (acI-A) and “Candidatus Nanopelagicus” (acI-B) and the proteobacterial genus “Candidatus Fonsibacter” (LD12). While “Ca. Planktophila” and “Ca. Fonsibacter” had no correlation in their natural habitat, they have the potential to be complementary in laboratory settings. We also investigated the bifunctional catalase-peroxidase enzyme KatG (a common good which “Ca. Planktophila” is dependent upon) and its most likely providers in the lake. Further, we found that while ecotype and community cooperation combined may explain “Ca. Planktophila” population abundance, the success of “Ca. Nanopelagicus” and “Ca. Fonsibacter” is better explained as a community by-product. Ecotype differentiation of “Ca. Fonsibacter” as a means of escaping predation was supported but not for overcoming auxotrophies.

IMPORTANCE This study examines evolutionary and ecological relationships of three of the most ubiquitous and abundant freshwater bacterial genera: “Ca. Planktophila” (acI-A), “Ca. Nanopelagicus” (acI-B), and “Ca. Fonsibacter” (LD12). Due to high abundance, these genera might have a significant influence on nutrient cycling in freshwaters worldwide, and this study adds a layer of understanding to how seemingly competing clades of bacteria can coexist by having different cooperation strategies. Our synthesis ties together network and ecological theory with empirical evidence and lays out a framework for how the functioning of populations within complex microbial communities can be studied.

Generalized estimating equation modeling on correlated microbiome sequencing data with longitudinal measures

Existing models for assessing microbiome sequencing such as operational taxonomic units (OTUs) can only test predictors' effects on OTUs. There is limited work on how to estimate the correlations between multiple OTUs and incorporate such relationship into models to evaluate longitudinal OTU measures. We propose a novel approach to estimate OTU correlations based on their taxonomic structure, and apply such correlation structure in Generalized Estimating Equations (GEE) models to estimate both predictors' effects and OTU correlations. We develop a two-part Microbiome Taxonomic Longitudinal Correlation (MTLC) model for multivariate zero-inflated OTU outcomes based on the GEE framework. In addition, longitudinal and other types of repeated OTU measures are integrated in the MTLC model. Extensive simulations have been conducted to evaluate the performance of the MTLC method. Compared with the existing methods, the MTLC method shows robust and consistent estimation, and improved statistical power for testing predictors' effects. Lastly we demonstrate our proposed method by implementing it into a real human microbiome study to evaluate the obesity on twins.

Gut microbial co-abundance networks show specificity in inflammatory bowel disease and obesity

The gut microbiome is an ecosystem that involves complex interactions. Currently, our knowledge about the role of the gut microbiome in health and disease relies mainly on differential microbial abundance, and little is known about the role of microbial interactions in the context of human disease. Here, we construct and compare microbial co-abundance networks using 2,379 metagenomes from four human cohorts: an inflammatory bowel disease (IBD) cohort, an obese cohort and two population-based cohorts. We find that the strengths of 38.6% of species co-abundances and 64.3% of pathway co-abundances vary significantly between cohorts, with 113 species and 1,050 pathway co-abundances showing IBD-specific effects and 281 pathway co-abundances showing obesity-specific effects. We can also replicate these IBD microbial co-abundances in longitudinal data from the IBD cohort of the integrative human microbiome (iHMP-IBD) project. Our study identifies several key species and pathways in IBD and obesity and provides evidence that altered microbial abundances in disease can influence their co-abundance relationship, which expands our current knowledge regarding microbial dysbiosis in disease.

Diverse Diets with Consistent Core Microbiome in Wild Bee Pollen Provisions

Bees collect pollen from flowers for their offspring, and by doing so contribute critical pollination services for our crops and ecosystems. Unlike many managed bee species, wild bees are thought to obtain much of their microbiome from the environment. However, we know surprisingly little about what plant species bees visit and the microbes associated with the collected pollen. Here, we addressed the hypothesis that the pollen and microbial components of bee diets would change across the range of the bee, by amplicon sequencing pollen provisions of a widespread small carpenter bee, Ceratina calcarata, across three populations. Ceratina calcarata was found to use a diversity of floral resources across its range, but the bacterial genera associated with pollen provisions were very consistent. Acinetobacter, Erwinia, Lactobacillus, Sodalis, Sphingomonas and Wolbachia were among the top ten bacterial genera across all sites. Ceratina calcarata uses both raspberry (Rubus) and sumac (Rhus) stems as nesting substrates, however nests within these plants showed no preference for host plant pollen. Significant correlations in plant and bacterial co-occurrence differed between sites, indicating that many of the most common bacterial genera have either regional or transitory floral associations. This range-wide study suggests microbes present in brood provisions are conserved within a bee species, rather than mediated by climate or pollen composition. Moving forward, this has important implications for how these core bacteria affect larval health and whether these functions vary across space and diet. These data increase our understanding of how pollinators interact with and adjust to their changing environment.